Characterization and recognition of handwritten digits using Julia

• URL: http://arxiv.org/abs/2102.11994v1
• Date: Wed, 24 Feb 2021 00:30:41 GMT
• Title: Characterization and recognition of handwritten digits using Julia
• Authors: M. A. Jishan, M. S. Alam, Afrida Islam, I. R. Mazumder, K. R. Mahmud and A. K. Al Azad
• Abstract summary: We implement a hybrid neural network model that is capable of recognizing the digit of MNISTdataset. The proposed neural model network can extract features from the image and recognize the features in the layer by layer. It also can recognize the auto-encoding system and the variational auto-encoding system of the MNIST dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic image and digit recognition is a computationally challenging task for image processing and pattern recognition, requiring an adequate appreciation of the syntactic and semantic importance of the image for the identification ofthe handwritten digits. Image and Pattern Recognition has been identified as one of the driving forces in the research areas because of its shifting of different types of applications, such as safety frameworks, clinical frameworks, diversion, and so on.In this study, for recognition, we implemented a hybrid neural network model that is capable of recognizing the digit of MNISTdataset and achieved a remarkable result. The proposed neural model network can extract features from the image and recognize the features in the layer by layer. To expand, it is so important for the neural network to recognize how the proposed modelcan work in each layer, how it can generate output, and so on. Besides, it also can recognize the auto-encoding system and the variational auto-encoding system of the MNIST dataset. This study will explore those issues that are discussed above, and the explanation for them, and how this phenomenon can be overcome.

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